Identifying“superspreaders”of disease is a pressing concern for society during pandemics such as COVID-19.Superspreaders represent a group of people who have much more social contacts than others.The widespread depl...Identifying“superspreaders”of disease is a pressing concern for society during pandemics such as COVID-19.Superspreaders represent a group of people who have much more social contacts than others.The widespread deployment of WLAN infrastructure enables non-invasive contact tracing via people’s ubiquitous mobile devices.This technology offers promise for detecting superspreaders.In this paper,we propose a general framework for WLAN-log-based superspreader detection.In our framework,we first use WLAN logs to construct contact graphs by jointly considering human symmetric and asymmetric interactions.Next,we adopt three vertex centrality mea-surements over the contact graphs to generate three groups of superspreader candidates.Finally,we leverage SEIR simulation to determine groups of superspreaders among these candidates,who are the most critical individuals for the spread of disease based on the simulation results.We have implemented our framework and evaluate it over a WLAN dataset with 41 million log entries from a large-scale university.Our evaluation shows superspread-ers exist on university campuses.They change over the first few weeks of a semester,but stabilize throughout the rest of the term.The data also demonstrate that both symmetric and asymmetric contact tracing can dis-cover superspreaders,but the latter performs better with daily contact graphs.Further,the evaluation shows no consistent differences among three vertex centrality measures for long-term(i.e.,weekly)contact graphs,which necessitates the inclusion of SEIR simulation in our framework.We believe our proposed framework and these results can provide timely guidance for public health administrators regarding effective testing,intervention,and vaccination policies.展开更多
基金the National Science Foundation(NSF)under Grant No.CNS 2028547.
文摘Identifying“superspreaders”of disease is a pressing concern for society during pandemics such as COVID-19.Superspreaders represent a group of people who have much more social contacts than others.The widespread deployment of WLAN infrastructure enables non-invasive contact tracing via people’s ubiquitous mobile devices.This technology offers promise for detecting superspreaders.In this paper,we propose a general framework for WLAN-log-based superspreader detection.In our framework,we first use WLAN logs to construct contact graphs by jointly considering human symmetric and asymmetric interactions.Next,we adopt three vertex centrality mea-surements over the contact graphs to generate three groups of superspreader candidates.Finally,we leverage SEIR simulation to determine groups of superspreaders among these candidates,who are the most critical individuals for the spread of disease based on the simulation results.We have implemented our framework and evaluate it over a WLAN dataset with 41 million log entries from a large-scale university.Our evaluation shows superspread-ers exist on university campuses.They change over the first few weeks of a semester,but stabilize throughout the rest of the term.The data also demonstrate that both symmetric and asymmetric contact tracing can dis-cover superspreaders,but the latter performs better with daily contact graphs.Further,the evaluation shows no consistent differences among three vertex centrality measures for long-term(i.e.,weekly)contact graphs,which necessitates the inclusion of SEIR simulation in our framework.We believe our proposed framework and these results can provide timely guidance for public health administrators regarding effective testing,intervention,and vaccination policies.
